A Symbolic Regression Method for Dynamic Modeling and Control of Quadrotor UAVs

TL;DR

This paper introduces a symbolic regression-based method to derive dynamic models of quadrotors from data and develops a hierarchical control scheme for trajectory tracking, validated through simulation experiments.

Contribution

It presents a novel data-driven approach for modeling quadrotor dynamics and a hierarchical control framework combining PI and backstepping methods.

Findings

Accurate dynamic models constructed solely from data.

Effective trajectory tracking demonstrated in simulations.

Hierarchical control improves convergence and stability.

Abstract

This paper presents a mathematic dynamic model of a quadrotor unmanned aerial vehicle (QUAV) by using the symbolic regression approach and then proposes a hierarchical control scheme for trajectory tracking. The symbolic regression approach is capable of constructing analytical quadrotor dynamic equations only through the collected data, which relieves the burden of first principle modeling. To facilitate position tracking control of a QUAV, the design of controller can be decomposed into two parts: a proportional-integral controller for the position subsystem is first designed to obtain the desired horizontal position and the backstepping method for the attitude subsystem is developed to ensure that the Euler angles and the altitude can fast converge to the reference values. The effectiveness is verified through experiments on a benchmark multicopter simulator.